A method for compiling severe flight spectra based on severe damage of mission profile

Abstract

The present invention discloses a method for compiling a severe flight spectrum based on severe damage of a mission profile, comprising: S1, determining the proportion and composition of an aircraft mission profile; S2, separating gusts and maneuvering overloads in measured overload time history data, and collecting peak and valley values; S3, determining a family of measured gust / maneuvering overload cumulative exceedance curves and distribution parameters for each mission segment; S4, simulating the Weibull distribution of the expected gust and maneuvering total damage of a fleet mission profile, and inverting to obtain a severe damage index D eq90 The coverage rate P of the cumulative transcendental number L ; S5. According to the distribution parameters, the coverage rate of each task segment at each level of overload is calculated as P L The exceedance number is obtained; the curve family of severe gust / maneuvering overload cumulative exceedance number under each mission segment is obtained; S6, the flight overload spectrum is compiled. The present invention simultaneously considers the dispersion of gust and maneuvering overload and load characteristics during the flight process, which can effectively reduce the fatigue test time and better reflect the load history of the fleet.

Description

Technical Field

[0001] The present invention relates to the technical field of flight spectrum compilation, and more particularly to a method for compiling a severe flight spectrum based on severe damage to a mission profile. Background Art

[0002] The flight load spectrum refers to the spectrum compiled from the load-time history experienced by the aircraft body during flight. It is compiled through special test modifications and flight test measurements. It is used to determine and verify the design service life of the aircraft and is the prerequisite for aircraft structure fatigue determination and life extension.

[0003] Even if the aircraft are used according to the same requirements, the load-time history of different aircraft in the fleet is significantly different, corresponding to the dispersion of the load spectrum, and durability analysis and testing must be carried out under a certain (unique) load spectrum. Therefore, how to select and compile a reasonable load spectrum has become the key to structural durability analysis and assessment. For this purpose, the "severity spectrum" is proposed. The severe spectrum has the advantages of being able to expose the failure characteristics of the aircraft itself and reduce the test time. Therefore, the severe spectrum is increasingly used in the compilation of today's measured spectra.

[0004] The current severe spectrum compilation method does not take into account the dispersion and load characteristics of gust overloads and maneuvering overloads in flight loads. Not only does the test take a long time, but it also fails to more accurately sense the load history of the fleet. Summary of the invention

[0005] In view of this, the present invention provides a method for compiling a severe flight spectrum based on severe damage to the mission profile, while taking into account the dispersion of gusts and maneuvering loads as well as load characteristics during flight, which can effectively reduce fatigue test time and better reflect the load history of the fleet.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for compiling a severe flight spectrum based on severe damage of a mission profile, comprising:

[0008] S1. Determine the proportion and composition of the aircraft mission profile, and determine the mission segment composition, sequence, proportion and parameters of each mission profile;

[0009] S2. Separate the gust overload and maneuvering overload in the measured overload time history data, use the limited span average peak counting method to count the peak and valley values ​​of the gust overload time history, and use the rain flow counting method to count the peak and valley values ​​of the maneuvering overload time history;

[0010] S3, fitting and solving the overload time history data processed by S2 to obtain the distribution parameters of each task segment;

[0011] S4. Simulate the expected damage distribution of the fleet, determine the severe damage index, and invert the coverage rate P of the cumulative exceedance number under the specified overload based on the severe damage index. L ;include:

[0012] S41. The Monte Carlo method is used to simulate the expected gust overload exceedance curve family and maneuvering overload exceedance curve family, and discretize them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i , n y,max is the load cycle peak value, n y,min is the load cycle valley value;

[0013] S42, according to the gust / maneuvering overload time series (n y,max , n y,min ) i Calculate the total overload damage D eq ;

[0014] S43, Assuming total overload damage D eq Following the Weibull distribution, the total damage D at each level of overload is fitted as follows: eqi And the corresponding empirical frequency value f i , calculate the Weibull distribution parameters α and β;

[0015] S44. Calculate the severe injury index D based on the Weil distribution parameters α and β eq90 , and invert the corresponding severe damage index D eq90 The coverage rate of the transcendental number P L The corresponding quantile u pL , and the corresponding transcendental coverage P L ; Severe injury index D eq90 The calculation formula is:

[0016] D eq90 =exp((ln(-ln(1-0.9))+αlnβ) / α)

[0017] Among them, the severe injury index D eq90 To achieve the expected service life requirements for 90% of the aircraft in the fleet eq ;

[0018] S5. According to the distribution parameters of each task segment, the coverage rate of each task segment under various levels of overload is calculated as P L The overload coverage ratio is P at each level. L The excess number of the severe gust overload is fitted to obtain the cumulative excess number curve family and maneuvering overload cumulative excess number curve under each mission segment;

[0019] S6. Prepare the flight overload spectrum based on the severe gust overload cumulative exceedance number curve family and the maneuvering overload cumulative exceedance number curve.

[0020] Furthermore, in S1, the mission segment parameters include: mission segment height, flight speed, weight, flight altitude and flight time; the mission segment division criteria are:

[0021] a) The flaps down approach start point is when the flaps angle reaches 35°;

[0022] b) The flap down approach end point / climb start point is determined by the flap deflection angle becoming 0°;

[0023] c) The end point of climb / the beginning point of level flight where the altitude curve turns flat shall be used as the criterion;

[0024] d) The end point of level flight / start point of descent is determined by the turning point of the descending altitude curve;

[0025] e) The end of the descent is determined by the flap angle becoming 35°.

[0026] Furthermore, S2 includes:

[0027] S21, standardize the measured overload time history data according to the following formula;

[0028] △n y0 =△n yi *Gi / G0

[0029] Among them, △n y0 is the overload value corrected according to the standard mission section; △n yi is the measured overload value, Gi is the real mass of the aircraft, and G0 is the standard aircraft mass under the current mission profile and the current mission segment;

[0030] S22, separating the maneuvering overload and the gust overload in the measured overload time history data after standardization according to a preset separation condition; the preset separation condition is:

[0031] a) If the overload change is less than the preset value and lasts for more than 2 seconds, it is considered as maneuvering overload; otherwise, it is considered as gust overload;

[0032] b) An overload with an absolute value of the rudder deflection angle equal to or greater than 4° is a gust overload, otherwise it is a maneuvering overload;

[0033] c) For the approach, departure, climb and descent missions, no separation of flight and gust is performed;

[0034] S23. Respectively detect the peaks and valleys of the measured maneuver overload time history data and gust overload time history data after separation, filter out the data between the peaks and valleys, and retain the peak points and valley points.

[0035] Further, S23 includes:

[0036] S21. Count the peaks and valleys of the gust overload time history data: Take the 0g overload state of each mission segment as the reference. When the valley between two peaks or the peak between two valleys does not exceed the upper deviation or lower deviation of the reference of this mission segment, only record the maximum peak or maximum valley among them; otherwise, record both peaks or valleys.

[0037] S232. Use the rain flow counting method to count the peaks and valleys of the maneuver overload time history data with the peak points and valley points retained.

[0038] Further, S232 includes:

[0039] S2321. Select the typical segments starting and ending at the maximum peak and valley, input each peak and valley in the order of the load spectrum until the data is complete.

[0040] S2322. Read in the next peak and valley. If the data is complete, stop reading.

[0041] S2323. If the number of data points of the peak and valley is less than 3, return to S2322; if the number of data points is greater than or equal to 3, calculate the range X and Y from the last 3 read peaks and valleys; among the three data points, the absolute value of the difference between the first point and the second point is Y; the absolute value of the difference between the second point and the third point is X.

[0042] S2324. Compare the magnitudes of X and Y. If X < Y, return to S2322; if X ≥ Y, execute S2325.

[0043] S2325. Record the range Y as a cycle, delete the peaks and valleys corresponding to Y, and return to S2322.

[0044] Further, S3 includes:

[0045] S31. Take the average of the peak count and valley count in the gust overload time history data as the positive overload cumulative exceedance number under gust overload; take the peak count and valley count in the maneuver overload time history data as the positive overload cumulative exceedance number and negative overload cumulative exceedance number under maneuver overload respectively.

[0046] S32. Standardize the overload cumulative exceedance numbers under gust overload and maneuver overload according to the following formula;

[0047]

[0048] Among them, t M is the measured flight time; t S is the standard time of the current task segment; N ib is the cumulative exceedance number of the i-th level load obtained during the measured flight time; N Yi is the cumulative exceedance number of the i-th load at the standard time;

[0049] S33, for each task segment overload cumulative exceedance data pair (Δn y ,N Y ) i Fitting is performed to obtain the overload cumulative exceedance curve of each task segment; the fitting equation is:

[0050] Δn y =a*lgN+b

[0051] Among them, a and b are the coefficients of the fitting equation; N is the data pair (Δn y ,N Y ) i ;

[0052] S34. Discretize the overload cumulative excess number curve and calculate the excess number ΔN i =N i -N i-1 , obtain the exceedance number data pairs corresponding to different flight overloads (Δn y , ΔN) i (i=1, ..., n);

[0053] S35, using the random variable model, assuming a specified overload Δn y The corresponding transcendental number ΔN follows a log-normal distribution;

[0054] S36. Solve the target distribution parameters μ and σ according to the likelihood function, and the solution equation is:

[0055]

[0056]

[0057] Where n is the number of different overload levels, μ is the logarithmic mean, and σ is the logarithmic standard deviation.

[0058] Furthermore, S41 includes:

[0059] S411, randomly draw a random number μ that follows a standard normal distribution p ~N(0,1), calculate the i-th level overload Δn yi The corresponding transcendental number ΔN i :

[0060]

[0061] S412, repeat the above sampling process to obtain the exceedance number ΔN of all level overloads in this task segment i .

[0062] S413, for ΔN i Cumulative counting to get the cumulative transcendental number The fitting equation in S35 was used to fit the data pair (Δn y ,N) i Fitting to obtain the Δn of this task segment y Cumulative transcendental number curve;

[0063] S414, repeating S411-S413 for all mission segments under the mission profile, to obtain a single-machine expected gust overload exceedance curve and a maneuvering overload exceedance curve under the mission profile represented by the sampling;

[0064] S415, discretize the obtained gust overload exceedance curve family and maneuvering overload exceedance curve family to obtain the exceedance data pairs (Δn y* ,ΔN) ij The data of the exceedance number corresponding to each level of maneuvering overload (+Δn y* ,ΔN) ij 、(-Δn y* ,ΔN) ij , where i is the discrete overload at each level and j is the task segment;

[0065] S416, respectively set the specified Δn of each task segment of gust overload and maneuvering overload y The exceedance numbers are summed to obtain the gust overload and its corresponding total exceedance number data pair. As well as the maneuvering overload and its corresponding total exceedance data and

[0066] S417, for gust overload and maneuvering overload, sort the positive overloads and negative overloads at all levels by their absolute values, and match them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i , n y,max is the load cycle peak value, n y,min is the load cycle valley value.

[0067] S418, repeat the above steps to obtain a family of time series of gusts and maneuvering overloads under the mission profile;

[0068] S419. If the aircraft has different mission profiles, repeat the above operation to obtain the time series family of gusts and maneuvering overloads under all mission profiles.

[0069] Furthermore, S42 includes:

[0070] S421, according to the gust / maneuvering overload time series (n y,max , n y,min ) i The single cycle equivalent damage is calculated and accumulated to obtain the gust overload spectrum damage and maneuvering overload spectrum damage; the calculation formula is:

[0071]

[0072] Among them, Δn eq is the equivalent load cycle amplitude; Δn is the load cycle amplitude, and n is the load cycle peak value. y,max Subtract the load cycle valley value n y,min Then divided by 2 to get; m is the damage index; d eq is the single cycle equivalent damage, and D is the load spectrum damage;

[0073] S422. Sum the gust overload spectrum damage and the maneuvering overload spectrum damage to obtain the total overload damage D eq . And invert the corresponding severe damage index D eq90 The coverage rate of the transcendental number P L The corresponding quantile u pL , and the corresponding transcendental coverage P L

[0074] Furthermore, S5 includes:

[0075] S51. Calculate the coverage rate of each task segment at each level of overload as P L The transcendental number of is calculated as:

[0076]

[0077] Among them, μ i , σ i are respectively the i-th level overload n y,i The logarithmic median and standard deviation of the corresponding transcendental numbers, μ PL is the transcendence coverage ratio P L The corresponding quantile;

[0078] S52, determine the data pairs (Δn y , ΔN PL ) i, and fitting is performed to obtain the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve corresponding to each mission segment, wherein the severe maneuvering overload cumulative exceedance curve includes: severe maneuvering positive overload cumulative exceedance curve and severe maneuvering negative overload cumulative exceedance curve;

[0079] S53. Perform high-load interception and low-load elimination on the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve of each mission section.

[0080] Furthermore, S6 includes:

[0081] S61, determining the load levels and corresponding cycle numbers of positive overload and negative overload of each task segment under each task profile, and the cycle numbers of positive overload and negative overload under each task segment are the same;

[0082] S62. Taking the flight / glide flight spectrum with the highest overload as a reference, assuming that the highest overload in each flight / glide segment is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight;

[0083] S63, linking various levels of overload and corresponding cycle numbers in a single takeoff and landing of the kth flight type of the i-th mission segment of the jth mission profile, randomly selecting peak values ​​and valley values ​​alternately for random pair arrangement, forming a severe overload spectrum of a specific flight type of the corresponding mission segment, and obtaining mission segment spectra of various flight types in each mission segment;

[0084] S64, sorting the mission segment spectrum under each mission profile according to the mission segment sequence under the mission profile, to obtain a mission profile spectrum under a complete flight;

[0085] S65. Sort all mission profile spectra of each complete flight to obtain a final flight overload spectrum.

[0086] Furthermore, in S63, the load spectrum of each mission segment for a specific flight type is recorded as (Δn ydn ,-Δn ydm ) h , and the task segment spectrum is expressed as the following load pair sequence:

[0087] f i,j,k =(Δn ydn ,-Δn ydm ) h

[0088] In the formula, Δn ydn The nth (n=1, 2, 3, 4, 5) positive overload of the mission segment represents the peak overload; -Δn ydmIt is the mth (m=1, 2, 3, 4, 5) level negative overload of this mission segment, representing the valley overload. For the level flight mission segment, the positive and negative overloads come from the gust / maneuvering overload. For the climb and descent mission segments, the positive and negative overloads come from the gust overload. For the departure and approach mission segments, the positive and negative overloads come from the maneuvering overload.

[0089] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for compiling a severe flight spectrum based on severe damage of a mission profile. In the process of compiling a severe spectrum of actual measured flight overloads of an aircraft, the dispersion and overload characteristics of gust overloads and maneuvering overloads in the flight loads are simultaneously considered. By reasonably arranging the positions of the two loads, gust and maneuvering, in the flight spectrum, the load history of the fleet can be better reflected. At the same time, the coverage rate of the cumulative exceedance number under a specified overload under the severe spectrum is inferred through severe damage. The severe flight overload spectrum finally compiled can reduce the time of fatigue testing, and the dispersion coefficient used in determining the service life only needs to consider the dispersion coefficient of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0091] Figure 1 A flow chart of a method for compiling a severe flight spectrum based on severe damage to a mission profile provided by the present invention;

[0092] Figure 2 A schematic diagram of peak-to-valley value detection provided by the present invention;

[0093] Figure 3 A schematic diagram of limiting the span-average peak count provided by the present invention;

[0094] Figure 4 A schematic diagram of the fitting results of the distribution parameter μ provided by the present invention;

[0095] Figure 5 A schematic diagram of the fitting results of the distribution parameter σ provided by the present invention;

[0096] Figure 6 A schematic diagram of the damage distribution of a cluster of machines provided by the present invention;

[0097] Figure 7 The severe cumulative exceedance curve of the mid-altitude level flight mission segment provided by the present invention. DETAILED DESCRIPTION

[0098] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] like Figure 1 As shown, an embodiment of the present invention discloses a method for compiling a severe flight spectrum based on severe damage to a mission profile, comprising:

[0100] S1. Determine the proportion and composition of the aircraft mission profile, and determine the mission segment composition, sequence, proportion and parameters of each mission profile;

[0101] S2. Separate the gust overload and maneuvering overload in the measured overload time history data, use the limited span average peak counting method to count the peak and valley values ​​of the gust overload time history, and use the rain flow counting method to count the peak and valley values ​​of the maneuvering overload time history;

[0102] S3, fitting and solving the overload time history data processed by S2 to obtain the distribution parameters of each task segment;

[0103] S4. Simulate the expected damage distribution of the fleet, determine the severe damage index, and invert the coverage rate P of the cumulative exceedance number under the specified overload based on the severe damage index. L ;include:

[0104] S41. The Monte Carlo method is used to simulate the expected gust overload exceedance curve family and maneuvering overload exceedance curve family, and discretize them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i , n y,max is the load cycle peak value, n y,min is the load cycle valley value;

[0105] S42, according to the gust / maneuvering overload time series (n y,max , n y,min ) i Calculate the total overload damage D eq ;

[0106] S43, Assuming total overload damage D eq Following the Weibull distribution, the total damage D at each level of overload is fitted as follows: eqi And the corresponding empirical frequency value f i , calculate the Weibull distribution parameters α and β;

[0107] S44. Calculate the severe injury index D based on the Weil distribution parameters α and β eq90 , and invert the corresponding severe damage index D eq90 The coverage rate of the transcendental number P L The corresponding quantile u pL , and the corresponding transcendental coverage P L ; Severe injury index D eq90 The calculation formula is:

[0108] D eq90 =exp((ln(-ln(1-0.9))+αlnβ) / α)

[0109] Among them, the severe injury index D eq90 To achieve the expected service life requirements for 90% of the aircraft in the fleet eq ;

[0110] S5. According to the distribution parameters of each task segment, the coverage rate of each task segment under various levels of overload is calculated as P L The coverage ratio under each level of overload is P L The excess number of the severe gust overload is fitted to obtain the cumulative excess number curve family and maneuvering overload cumulative excess number curve under each mission segment;

[0111] S6. Prepare the flight overload spectrum based on the severe gust overload cumulative exceedance number curve family and the maneuvering overload cumulative exceedance number curve.

[0112] The above steps are further explained below.

[0113] In a specific embodiment, S1, determining a mission profile:

[0114] The mission profile ratio and mission profile composition, and the parameters of each mission segment in the mission profile are given. The mission segment parameters include: mission segment height, flight speed, weight, flight altitude and flight time; the division criteria of the mission segment are:

[0115] a) The flaps down approach start point is when the flaps angle reaches 35°;

[0116] b) The flap down approach end point / climb start point is determined by the flap deflection angle becoming 0°;

[0117] c) The end point of climb / the beginning point of level flight where the altitude curve turns flat shall be used as the criterion;

[0118] d) The end point of level flight / start point of descent is determined by the turning point of the descending altitude curve;

[0119] e) The end of the descent is determined by the flap angle becoming 35°.

[0120] In one embodiment, S2, preprocessing of measured overload data, specifically includes:

[0121] S21, overload data n y Standardization processing:

[0122] The measured overload time history data is standardized according to the following formula;

[0123] △n y0 =△n yi *Gi / G0

[0124] Among them, △n y0 is the overload value corrected according to the standard mission section; △n yi is the measured overload value, Gi is the real mass of the aircraft, G0 is the standard aircraft mass under the current mission profile and the current mission segment; the measured center of gravity overload n y Subtract 1 to get the incremental overload Δn at that moment y The true mass data is given by subtracting the fuel consumption from the aircraft weight, and the fuel consumption is obtained by total average calculation (the average fuel consumption is calculated from the start of takeoff taxiing to the end of landing impact).

[0125] S22. Separation of flight overload and gust overload:

[0126] The maneuvering overload and gust overload in the measured overload time history data after standardization are separated according to the preset separation conditions; the preset separation conditions are:

[0127] a) If the overload change is less than the preset value and lasts for more than 2 seconds, it is considered as maneuvering overload; otherwise, it is considered as gust overload;

[0128] b) Overloads with an absolute value of the rudder deflection angle equal to or greater than 4° are gust overloads, otherwise they are maneuvering overloads;

[0129] c) For the approach, departure, climb and descent missions, the flight and gust overload separation is not performed; for the approach and departure missions, due to the small number of gust overloads, all overloads are approximately considered as maneuvering overloads, and the maneuvering overload and gust overload separation are no longer performed. For the climb and descent missions, due to the small number of maneuvering movements, all overloads are approximately considered as gust overloads, and the maneuvering overload and gust overload separation are no longer performed.

[0130] S23, peak and valley value collection:

[0131] The peak and valley values ​​of the separated measured maneuvering overload time history data and gust overload time history data are detected respectively, the data between the peak and valley values ​​are filtered out, and the sampling point numbers of the peak and valley points are retained.

[0132] Specifically: Figure 2As shown, retain Δn z1 , Δn z3 , Δn z5 , Δn z6 , Δn z7 , Δn z8 , Δn z10 Peak and valley points, remove Δn z2 , Δn z4 , Δn z9 Sampling point.

[0133] If satisfied

[0134]

[0135] Then take the peak value (or valley value) once.

[0136] Specifically, S23 includes:

[0137] S231, gust overload time history count:

[0138] Count the peaks and valleys of the gust overload time history data: take the 0g overload state of each task segment as the benchmark. When the valley value between two peaks or the peak value between two valley values ​​does not exceed the upper deviation (positive overload) or lower deviation (negative overload) of the benchmark of the task segment, only record the maximum peak value (positive overload) or maximum valley value (negative overload). Otherwise, record both peaks or valley values.

[0139] The method can be generalized to limit the cross-average peak count, such as Figure 3 As shown, the upper and lower deviations are set to 20% of the maximum peak value, and this deviation is the limiting condition. In the figure, the hollow points are counting points, and the solid points are filtering points.

[0140] S232, maneuvering overload time history count:

[0141] The rain flow counting method is used to count the peak and valley values ​​of the maneuvering overload time history data with peak and valley points retained; specifically:

[0142] S2321, select a typical segment starting and ending at the maximum peak and valley value, and input each peak and valley value in the order of the load spectrum until the data is completed;

[0143] S2322, read in the next peak and valley value, and stop reading if the data is complete;

[0144] S2323, if the number of data points of the peak and valley values ​​is less than 3, return to S2322; if the number of data points is greater than or equal to 3, calculate the ranges X and Y based on the last three peak and valley values ​​read in; among the three data points, the absolute value of the difference between the first point and the second point is Y; the absolute value of the difference between the second point and the third point is X;

[0145] S2324. Compare the magnitudes of X and Y. If X < Y, return to S2322; if X ≥ Y, execute S2325.

[0146] S2325. Denote the variable range Y as a cycle, delete the peak and valley values corresponding to Y, and return to S2322.

[0147] In one embodiment, S3 includes:

[0148] S31. Obtaining the cumulative exceedance numbers of each level of overload:

[0149] Take the average of the peak count and valley count in the gust overload time history data as the positive overload cumulative exceedance number under gust overload; take the peak count and valley count in the maneuver overload time history data as the positive overload cumulative exceedance number and negative overload cumulative exceedance number under maneuver overload, respectively.

[0150] Specifically:

[0151] Select several levels of overload, and record the positive flight overload (gust overload and maneuver overload) cumulative exceedance numbers and negative flight overload cumulative exceedance numbers for the positive peaks and negative valleys respectively, denoted as N b (+Δn y ) and Nb(-Δn y ).

[0152] For gust overload, the discrete gust model believes that for gust disturbances of the same intensity (i.e., the same amplitude), the occurrence probabilities of positive (upward) and negative (downward) are equal. Therefore, theoretically, the measured positive and negative gust overload cumulative exceedance number curves should be symmetric. However, in actual processes, there are often certain differences between the positive and negative gust cumulative exceedance number curves. Geometric mean is used in engineering to obtain a symmetric gust overload corresponding cumulative exceedance number. In this way, the cumulative exceedance number curves of ±Δn y are obtained, as shown in the following formula.

[0153]

[0154] For maneuver overload, it is generally not considered that for flight overloads of the same intensity, the occurrence probabilities of positive (upward) and negative (downward) are equal. Therefore, for maneuver overload, the positive overload cumulative exceedance number and negative overload cumulative exceedance number need to be counted separately.

[0155] S32. Standardization processing of the overload cumulative exceedance number considering the flight duration:

[0156] Since there is a difference between the measured flight time of the load spectrum and the standard time of the mission segment in the mission profile, it is necessary to convert the measured mission segment overload cumulative exceedance number data into cumulative exceedance number data of the standard time.

[0157] The overload cumulative exceedance number under gust overload and maneuvering overload is standardized according to the following formula;

[0158]

[0159] Among them, t M is the measured flight time; t S is the standard time of the current task segment; N ib is the cumulative exceedance number of the i-th level load obtained during the measured flight time; N Yi is the cumulative exceedance number of the i-th load at the standard time;

[0160] The cumulative exceedance numbers of each level of overload are processed according to the above formula to obtain the cumulative exceedance number data of the task segment overload of standard time.

[0161] S33, fitting of overload cumulative exceedance curve:

[0162] For each mission segment, the cumulative overload exceedance data pair (Δn y ,N Y ) i Fitting is performed to obtain the overload cumulative exceedance curve of each mission segment, including the gust overload cumulative exceedance curve and the maneuvering overload (positive / negative) cumulative exceedance curve; the fitting equation is:

[0163] Δn y =a*lgN+b

[0164] Among them, a and b are the coefficients of the fitting equation; N is the data pair (Δn y ,N Y ) i ;

[0165] S34, Gradual discreteness of overload cumulative exceedance curve:

[0166] Discretize the overload cumulative exceedance curve into (Δn y , N) i (i=1,…,n) data pairs, calculate the transcendental number ΔN i =N i -N i-1 , obtain the exceedance number data pairs corresponding to different flight overloads (Δn y , ΔN) i (i=1,…,n).

[0167] S35, Cumulative Exceedance Distribution Characteristics Test:

[0168] Using the random variable model, assuming a specified overload Δn yThe corresponding transcendental number ΔN obeys the log-normal distribution.

[0169] S36. Distribution parameter estimation:

[0170] For a specific task segment, when the measured sample is large, it is recommended to use the maximum likelihood estimation method to estimate the distribution parameters. The likelihood function is shown as follows

[0171]

[0172] Where x i =lgΔN i . According to the likelihood function, the derivatives of μ and σ are zero to solve the target parameters (μ, σ). The solution equation is:

[0173]

[0174]

[0175] Where n is the number of different overload levels, μ is the logarithmic mean, and σ is the logarithmic standard deviation.

[0176] In one embodiment, S4 includes:

[0177] S41. Single-machine mission segment gust exceedance curve and maneuver exceedance curve:

[0178] The Monte Carlo method is used to simulate the expected gust overload exceedance curve family and maneuvering overload exceedance curve family, and discretize them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i ; Specifically:

[0179] S411, randomly draw a random number μ that follows a standard normal distribution p ~N(0,1), calculate the i-th level overload Δn yi The corresponding transcendental number ΔN i :

[0180]

[0181] S412, repeat the above sampling process to obtain the exceedance number ΔN of all level overloads in this task segment i .

[0182] S413, for ΔN i Cumulative counting to get the cumulative transcendental number The fitting equation in S35 was used to fit the data pair (Δn y ,N) i Fitting to obtain the Δn of this task segment y Cumulative transcendental number curve;

[0183] S414, repeating S411-S413 for all mission segments under the mission profile, to obtain a single-machine expected gust overload exceedance curve and a maneuvering overload exceedance curve under the mission profile represented by the sampling;

[0184] S415, discretize the obtained gust overload exceedance curve family and maneuvering overload exceedance curve family to obtain the exceedance data pairs (Δn y* ,ΔN) ij The data of the exceedance number corresponding to each level of maneuvering overload (+Δn y* ,ΔN) ij 、(-Δn y* ,ΔN) ij , where i is the discrete overload at each level and j is the task segment;

[0185] S416, respectively set the specified Δn of each task segment of gust overload and maneuvering overload y The exceedance numbers are summed to obtain the gust overload and its corresponding total exceedance number data pair. As well as the maneuvering overload and its corresponding total exceedance data and

[0186] S417, for gust overload and maneuvering overload, sort the positive overloads and negative overloads at all levels by their absolute values, and match them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i , n y,max is the load cycle peak value, n y,min is the load cycle valley value.

[0187] S418, repeat the above steps to obtain a family of time series of gusts and maneuvering overloads under the mission profile;

[0188] S419. If the aircraft has different mission profiles, repeat the above operation to obtain the time series family of gusts and maneuvering overloads under all mission profiles.

[0189] S42, according to the gust / maneuvering overload time series (n y,max , n y,min ) i Calculate the total overload damage D eq ; Specifically:

[0190] S421, according to the gust / maneuvering overload time series (n y,max , n y,min ) iThe single cycle equivalent damage is calculated and accumulated to obtain the gust overload spectrum damage and maneuvering overload spectrum damage; the calculation formula is:

[0191]

[0192] Among them, Δn eq is the equivalent load cycle amplitude; Δn is the load cycle amplitude, and n is the load cycle peak value. y,max Subtract the load cycle valley value n y,min Then divided by 2 to get; m is the damage index, that is, the slope of the material / structure fatigue SN curve under R = 0. For aluminum alloy materials, it can be approximately taken as 4; d eq is the single cycle equivalent damage, and D is the load spectrum damage;

[0193] S422. Sum the gust overload spectrum damage and the maneuvering overload spectrum damage to obtain the total overload damage D eq .

[0194] S43. Statistical analysis of fleet damage:

[0195] Assuming the total overload damage D eq It obeys the Weibull distribution and uses the rank statistics method to calculate the distribution parameters, as shown in the following formula, to fit the total damage D at each level of overload eqi And the corresponding empirical frequency value f i , calculate the Weibull distribution parameters α and β;

[0196] ln(-ln(1-f i ))=αlnD eqi -αlnβ

[0197] S44. Calculate the severe injury index D based on the Weil distribution parameters α and β eq90 , and invert the corresponding severe damage index D eq90 The coverage rate of the transcendental number P L The corresponding quantile u pL ; Severe injury index D eq90 The calculation formula is:

[0198] D eq90 =exp((ln(-ln(1-0.9))+αlnβ) / α)

[0199] Among them, the severe injury index D eq90 To enable 90% of the aircraft in the fleet to reach their expected service life requirements eq .

[0200] The severity spectrum should meet the requirements of GJB 67.6A-2008 "Military Aircraft Strength Specifications: Repeated Loads, Durability and Damage Tolerance" on the durability severity spectrum. According to the requirements of this standard, the durability severity spectrum should reflect 90% of the usage of the fleet, so the damage covering 90% of the fleet is taken here as the severity damage indicator.

[0201] In one embodiment, S5, determining a severe cumulative exceedance curve, specifically includes:

[0202] S51. Calculate the coverage rate of each task segment at each level of overload as P L The transcendental number of is calculated as:

[0203]

[0204] Among them, μ i , σ i are respectively the i-th level overload n y,i The logarithmic median and standard deviation of the corresponding transcendental numbers, μ PL is the transcendence coverage ratio P L The corresponding quantile;

[0205] The coverage rate P L It should be obtained by inversion of severe damage, that is, it should be repeatedly tried to find P L until the damage corresponding to the overload cumulative exceedance curve family under this coverage ratio is equal to the severe damage of the mission profile.

[0206] S52, determine the data pairs (Δn y , ΔN PL ) i , and fitting is performed to obtain the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve corresponding to each mission segment, wherein the severe maneuvering overload cumulative exceedance curve includes: severe maneuvering positive overload cumulative exceedance curve and severe maneuvering negative overload cumulative exceedance curve;

[0207] S53. Perform high-load interception and low-load interception on the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve of each mission segment, including:

[0208] (1) High load interception

[0209] The high load in the overload spectrum has two effects: on the one hand, it will increase fatigue damage, so it is necessary to appropriately add high loads to the spectrum according to the overload spectrum; on the other hand, the coupling effect of more high loads and low loads will increase the life. Therefore, it is necessary to determine the level and number of high loads in the overload spectrum, usually taking the high load that appears once in 1000 flights in this mission segment as the cutoff value;

[0210] (2) Low load cutoff

[0211] The spectrum usually contains a large number of small overload cycles, which need to be deleted or converted into a certain level of overload. The fatigue limit of the key parts is usually determined based on the above analysis, and the overload value corresponding to 70% to 80% of the fatigue limit is truncated.

[0212] In a specific embodiment, S6 includes:

[0213] S61. Determination of overload level based on mission profile:

[0214] Determine the load levels and corresponding cycles of positive overload and negative overload of each task segment under each task profile, and the number of cycles of positive overload and negative overload under each task segment is the same; specifically include:

[0215] (1) Determination of load level:

[0216] The overload of each mission segment is discrete into 5 levels. The number of overloads after discretization is an integer in a program block. The number of flights of each mission segment is not less than 1 time. The representative value of each level of overload (i.e. equivalent overload) is determined by the discrete segment equal damage conversion.

[0217] (2) Determination of load parameters at each level of the 5×5 spectrum

[0218] Based on the mission segment severe overload cumulative exceedance curve, the load spectrum equivalent calculation is performed. Calculation of equivalent load: Assume that the equivalent load of a discrete segment to be calculated is Δn yd , the equivalent load cycle number is N eq , m straight lines are used to replace the curve of the discrete segment, and the linear equation of the i-th segment is:

[0219] Δg=a i lgN+b i

[0220] In the formula, a i 、b i is the constant of the i-th load spectrum curve.

[0221] Then the equivalent load is:

[0222]

[0223] Where:

[0224]

[0225] Wherein, S is the slope parameter of the material SN curve, and for aluminum alloy, S=2.0.

[0226] Equivalent load cycles

[0227]

[0228] A total of 5 levels of equivalent load Δn are obtained ydi (i = 1, 2, 3, 4, 5) and the corresponding number of cycles N eqi (i=1, 2, 3, 4, 5). The same applies to negative overload.

[0229] It should be noted that for each mission segment of maneuvering overload, it is necessary to ensure that the total number of cycles of positive overload and negative overload in the mission segment is equal, so that positive and negative overloads can be paired without generating unnecessary peaks or valleys. The Δn with the minimum absolute value of negative maneuvering overload can be modified. yd , so that the corresponding N eq changes to meet the positive and negative overload equal.

[0230] S62. Determination of flight type:

[0231] Taking the flight / glide flight spectrum with the highest overload as the benchmark, assuming that the highest overload in each flight / glide segment is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight;

[0232] In this embodiment, the load spectra of the mid-altitude flight mission profile are compiled according to five different flight types. The principle for determining the typical flight type of mid-altitude flight is: taking the flight spectrum of the flight (glide) segment with the highest load as the benchmark, and assuming that the highest load in each flight (glide) segment is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight (determine y i ); Based on the assumption that the flight spectra of various flight types are similar in shape, the flight incremental overload spectra of various types of flights under 1000 flights are compiled (to determine B ij ); as shown in Table 1.

[0233] Table 1 5×5 spectrum

[0234]

[0235]

[0236] In the table, y1+y2+y3+y4+y5 should be equal to the number of times the profile appears in 1000 flights. Use the above method to compile the 5×5 spectra of various mission segments under all profiles. And the y1, y2, y3, y4, and y5 of the 5×5 spectra of each mission segment under each mission profile are the same; for the 5×5 spectra of positive / negative overload under each mission segment, the same.

[0237] S63, Task Segment Compilation:

[0238] The various levels of overload and their corresponding cycle times in a single takeoff and landing of the kth flight type of the i-th mission segment of the jth mission profile are linked, and the peak and valley values ​​are randomly selected and randomly arranged in pairs to form a severe overload spectrum of a specific flight type in the corresponding mission segment, and the mission segment spectrum of various flight types in each mission segment is obtained. Specifically:

[0239] According to the data in the compiled 5×5 spectrum, the various levels of overload and their corresponding frequencies under the single takeoff and landing of the kth flight type in the i-th mission segment of the jth profile are linked, and the peak and valley values ​​are randomly selected alternately (i.e., the peak and valley values ​​are random) and randomly arranged in pairs to form the load spectrum of the specific flight type of the mission segment, which is recorded as (Δn ydn ,-Δn ydm ) h , and the task segment spectrum (a total of j×i×k) is expressed as the following load pair sequence

[0240] f i,j,k =(Δn ydn ,-Δn ydm ) h

[0241] In the formula, Δn ydm The nth (n=1, 2, 3, 4, 5) positive overload of the mission segment represents the peak overload; -Δn ydm The mth (m = 1, 2, 3, 4, 5) negative overload of the mission segment represents the valley overload (for the level flight mission segment, the positive and negative overloads come from the gust / maneuvering overloads, for the climb and descent mission segments, the positive and negative overloads come from the gust overloads, for the departure and approach mission segments, the positive and negative overloads come from the maneuvering overloads). The total number of overload pairs for each mission segment of each flight type should be kept equal to the total number of cycles per flight in Table 1 above. According to this method, the spectra (load pair sequences) of various flight types under all mission segments are compiled.

[0242] S64, Mission Profile Compilation:

[0243] According to the order of the mission segments under each mission profile, the mission segment spectrum under the mission profile is sorted to obtain the mission profile spectrum under a complete flight; specifically:

[0244] The load spectrum of the kth flight type of the jth profile is F j,k . F j,k It can be expressed as a load pair sequence f i,j,k Sort by the order of the task segments i (i = 1, 2, 3 ... m) under the profile, that is, f 1,j,k ,f 2,j,k ,f 3,j,k ,f 4,j,k …f m,j,kIn this way, the mission profile spectrum of a complete flight is obtained. According to the above method, the mission profile spectrum of various flight types under all mission profiles is compiled. All types of mission profile spectra are expressed in vector form as

[0245] B=[F 1,1 … F 1,K F 2,1 … F 2,K F 3,1 … F 3,K …… F L,1 … F L,K ]

[0246] There are five mission segments under the hollow profile, and five flight types in each mission segment. Therefore, for the hollow profile, a total of five mission profile spectra need to be compiled, representing five flight types. They are recorded as A1, B1, C1, D1, and E1. When compiling the A1 spectrum, arrange the spectra of flight type A under each mission segment, that is, f 1,j,k ,f 2,j,k ,f 3,j,k , we can get the A1 spectrum, and the same goes for other types of mission profile spectra.

[0247] S65. Final flight overload spectrum compilation:

[0248] All mission profile spectra of each complete flight are sorted to obtain the final flight overload spectrum.

[0249] Specifically, record the number of times y appears in the jth profile (j=1,2...L) and the kth flight type (k=1,2...K) in 1000 flights. j,k times. j,k Expressed in vector form as

[0250] A=[y 1,1 … y 1,K y 2,1 … y 2,K y 3,1 … y 3,K ... y L,1 … y L,K ] (16)

[0251] Where A is the vector of the number of occurrences of various mission profiles.

[0252] The A vector and the B vector are in a one-to-one correspondence, indicating how many times the task profile spectrum appears in this 1000-time block spectrum. The total spectrum can be expressed as the task profile spectrum sequence G

[0253] G=A*B=[F 1,1 F1,1 F 1,1 F 1,1 ……F 1,2 F 1,2 F 1,2 F 1,2 ……F L,K F L,K F L,K F L,K ]

[0254] The final flight-continuation-flight spectrum can be obtained by reordering the mission profile spectrum of each complete flight in G. The specific method is as follows: first, all the mission profiles of G are numbered in sequence with natural numbers, and then the numbers are respectively compared with the random integer sequence "x is "Correspondingly, press x is Sort by the numerical size (for example, the corresponding x is is 5, then the corresponding mission profile spectrum is placed at the 5th position) thus obtaining the final load spectrum.

[0255] The random number sequence is obtained by using the pseudo-random method of multiplication congruential method, and reasonable random results are given by adjusting random parameters.

[0256] Multiplication by congruence method:

[0257] y i+1 =λy i (mod2 k )

[0258] x i =y i / 2 m

[0259] First, form "y i "Sequence, and then get the random sequence "x i Calculate x i In the order of the total number column from smallest to largest x is , thus we get the random integer sequence "x is ”.

[0260] The initial value y1 is an odd number; the coefficient λ = 8t-3, t is an arbitrary natural number, k is determined according to the random period "n" required, so that 2 k-2 ≥n; m is 2. By changing the size of y1, t, k, different random number sequences can be obtained.

[0261] The present invention will be further described below with reference to specific examples.

[0262] S1. Determine the mission profile

[0263] The typical mission profile of a certain type of aircraft is medium-altitude flight, and the compilation is based on 1,000 takeoffs and landings.

[0264] Table 2 Task sections of hollow sections

[0265] Task segment Time (min) Flaps down departure 1.9 Climb 2.1 Flying in the air 10.1 Downward 2.3 Flaps down approach 2.2

[0266] S2. Preprocessing of measured overload data:

[0267] A total of 10 takeoff and landing flight parameter data of an aircraft were measured. The flight parameter data provided included time, flight altitude, y-axis overload at the center of gravity, remaining fuel in the left / right engine, flap deflection angle, and elevator deflection angle.

[0268] The measured overload data is standardized according to the S21 method to obtain the standard quality of each task segment as shown in Table 3.

[0269] Table 3 Standard quality of each task segment

[0270] Task segment Total mass (kg) Exit 22500 Climb 22000 Flying in the air 20000 Downward 15000 Approach 14500

[0271] Peak and valley value collection:

[0272] Before counting statistics, the peak-valley value detection of the statistical parameters is performed according to S23 to obtain the flight overload peak-valley value data pair (Δn y峰 , Δn y谷 ) i .

[0273] S3. Statistics of overload cumulative exceedance curve family:

[0274] With Δn y =0.05g, and the interval is 0.05g. The method in Section 4.3.1 is used to count the standardized overload time history, and the flight overload peak and valley value data pairs (Δn y峰 , Δn y谷 ) i .

[0275] The cumulative overload exceedances of the positive peak overload and the negative valley overload were counted, and the flight duration was considered. The cumulative overload exceedances of each level were standardized. The distribution parameters of the overload exceedances of each level were estimated. The results are shown in Figure 4-5 shown.

[0276] S4. Expected damage distribution of simulated fleet. The damage distribution of mid-air mission profile is as follows: Figure 6 The Weir distribution parameters are shown in Table 4:

[0277] Table 4 Weibull distribution parameters

[0278] α β 0.5350 1.86

[0279] The severe damage value corresponding to 90% reliability is 8.6025. The corresponding μ is obtained by damage inversion. p =1.2596.

[0280] S5. The severe cumulative exceedance curve of the medium-altitude level flight mission is as follows Figure 7 shown.

[0281] The high-load cutoff values ​​and low-load cutoff values ​​of the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve of each mission segment are shown in Tables 5 and 6.

[0282] Table 5 High load cutoff values ​​for level flight mission segment

[0283] Task segment High load cutoff value / g Level flight 0.98

[0284] Table 6 Low load deletion values ​​for level flight mission segment

[0285] Task segment Low load deletion value / g Level flight 0.17

[0286] S6. Overload spectrum drawing

[0287] According to the compilation principles, the flight type numbers of the severe flight spectrum and the medium-altitude flight profile are given respectively. The flight type numbers of the severe flight spectrum are shown in Table 7, and the flight spectrum of the climb mission segment under the medium-altitude flight profile is shown in Tables 8-10.

[0288] Table 7 Flight type number of typical flight profiles (1000 flights)

[0289]

[0290]

[0291] Table 8 Wind gust spectrum for medium-altitude flight

[0292]

[0293] Table 8 Overload spectrum of positive maneuver during medium altitude flight (+)

[0294]

[0295] Table 9 Negative maneuvering overload spectrum during medium altitude flight (-)

[0296]

[0297] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0298] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for compiling a severe flight spectrum based on severe damage to mission profile, characterized in that: Including: S1. Determine the proportion and composition of the aircraft mission profile, and determine the mission segment composition, sequence, proportion, and parameters of each mission profile; S2. Separate the gust overload and maneuver overload in the measured overload time history data. Use the restricted cross-average peak counting method to count the peak and valley values of the gust overload time history, and use the rainflow counting method to count the peak and valley values of the maneuver overload time history; S3. Fit and solve the overload time history data processed in S2 to obtain the distribution parameters of each mission segment; S4. Simulate the expected damage distribution of the fleet, determine the severe damage index, and invert the coverage rate P of the cumulative exceedance number under the specified overload based on the severe damage index. L ; Including: S41. The Monte Carlo method is used to simulate the expected gust overload exceedance curve family and maneuvering overload exceedance curve family, and discretize them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i , n y,max is the load cycle peak value, n y,min is the load cycle valley value; S42, according to the gust / maneuvering overload time series (n y,max , n y,min ) i Calculate the total overload damage D eq ; S43, Assuming total overload damage D eq Following the Weibull distribution, the total damage D at each level of overload is fitted as follows: eqi And the corresponding empirical frequency value f i , calculate the Weibull distribution parameters α and β; S44. Calculate the severe injury index D based on the Weil distribution parameters α and β eq90 , and invert the corresponding severe damage index D eq90 The coverage rate of the transcendental number P L The corresponding quantile u pL , and the corresponding transcendental coverage P L ; Severe injury index D eq90 The calculation formula is: D eq90 =exp((ln(-ln(1-0.9))+αlnβ) / α) Among them, the severe injury index D eq90 To achieve the expected service life requirements for 90% of the aircraft in the fleet eq ; S5. According to the distribution parameters of each task segment, the coverage rate of each task segment under various levels of overload is calculated as P L The overload coverage ratio at each level is P L The excess number of the severe gust overload is fitted to obtain the cumulative excess number curve family and maneuvering overload cumulative excess number curve under each mission segment; S6. Compile the flight overload spectrum according to the severe gust overload cumulative exceedance number curve family and the maneuver overload cumulative exceedance number curve.

2. The method for compiling a severe flight spectrum based on severe damage to a mission profile according to claim 1, characterized in that S2 Including: S21. Standardize the measured overload time history data according to the following formula; △n y0 =△n yi *Gi / G0 Among them, △n y0 is the overload value corrected according to the standard mission section; △n yi is the measured overload value, Gi is the real mass of the aircraft, and G0 is the standard aircraft mass under the current mission profile and the current mission segment; S22. Separate the maneuver overload and gust overload in the measured overload time history data processed by standardization according to the preset separation conditions; The preset separation conditions are: a) When the overload change is less than the preset value and the duration exceeds 2 s, it is maneuver overload, otherwise it is gust overload; b) The overload with the absolute value of the rudder surface deflection angle equal to or greater than 4° is gust overload, otherwise it is maneuver overload; c) For the approach, departure, climb, and descent mission segments, no separation of flight and gust is performed; S23. Detect the peaks and valleys of the separated measured maneuver overload time history data and gust overload time history data respectively, filter out the data between the peaks and valleys, and retain the peak points and valley points.

3. The method for compiling a severe flight spectrum based on severe damage to mission profile according to claim 2, characterized in that: S23 includes: S231. Count the peaks and valleys of the gust overload time history data: Take the 0g overload state of each mission segment as the reference. When the valley between two peaks or the peak between two valleys does not exceed the upper deviation or lower deviation of the reference of this mission segment, only record the maximum peak or maximum valley among them, otherwise, record two peaks or valleys; S232. Use the rainflow counting method to count the peaks and valleys of the maneuver overload time history data retaining the peak points and valley points.

4. The method for compiling a severe flight spectrum based on severe damage to mission profile according to claim 3, characterized in that: S232 includes: S2321. Select the typical segments starting and ending at the maximum peaks and valleys, input each peak and valley value in the order of the load spectrum until the data is completed; S2322. Read in the next peak and valley value. If the data is completed, stop reading; S2323. If the number of data points of the peak and valley values is less than 3, return to S2322; If the number of data points is greater than or equal to 3, calculate the range X and Y from the last 3 peak and valley values read in; Among the three data points, the absolute value of the difference between the first point and the second point is Y; The absolute value of the difference between the second point and the third point is X; S2324. Compare the magnitudes of X and Y. If X < Y, return to S2322. If X ≥ Y, execute S2325; S2325. Record the range Y as a cycle, delete the peak and valley values corresponding to Y, and return to S2322.

5. The method for compiling a severe flight spectrum based on severe damage to mission profile according to claim 4, characterized in that S3 Including: S31. Take the average of the peak count and valley count in the gust overload time history data as the positive overload cumulative exceedance number under gust overload; Take the peak count and valley count in the maneuver overload time history data as the positive overload cumulative exceedance number and negative overload cumulative exceedance number under maneuver overload respectively; S32. Standardize the cumulative overload exceedance under gust overload and maneuvering overload according to the following formula; Among them, t M is the measured flight time; t S is the standard time of the current task segment; N ib is the cumulative exceedance number of the i-th level load obtained during the measured flight time; N Yi is the cumulative exceedance number of the i-th load at the standard time; S33, for each task segment overload cumulative exceedance data pair (Δn y ,N Y ) i Fitting is performed to obtain the overload cumulative exceedance curve of each task segment; the fitting equation is: Δn y =a*lgN+b Among them, a and b are the coefficients of the fitting equation; N is the data pair (Δn y ,N Y ) i ; S34. Discretize the overload cumulative excess number curve and calculate the excess number ΔN i =N i -N i-1 , obtain the exceedance number data pairs corresponding to different flight overloads (Δn y , ΔN) i (i=1, ..., n); S35, using the random variable model, assuming a specified overload Δn y The corresponding transcendental number ΔN follows a log-normal distribution; S36. Solve the target distribution parameters μ and σ according to the likelihood function, and the solution equation is: Where n is the number of different overload levels, μ is the logarithmic mean, and σ is the logarithmic standard deviation.

6. The method for compiling a severe flight spectrum based on severe damage to mission profile according to claim 1, characterized in that: S41 includes: S411, randomly draw a random number u that follows a standard normal distribution p ~N(0,1), calculate the i-th level overload Δn yi The corresponding transcendental number ΔN i : S412, for ΔN i Cumulative counting to get the cumulative transcendental number For data pairs (Δn y ,N) i Fitting to obtain the Δn of a single takeoff and landing y Cumulative transcendental number curve; S413, repeating S411-S412 for all mission segments, and obtaining the expected gust overload exceedance curve and maneuvering overload exceedance curve of the single machine represented by the sampling; S414, discretize the obtained gust overload exceedance curve family and maneuvering overload exceedance curve family to obtain the exceedance data pairs (Δn y* ,ΔN) ij The data of the exceedance number corresponding to each level of maneuvering overload (+Δn y* ,ΔN) ij 、(-Δn y* ,ΔN) ij , where i is the discrete overload at each level and j is the task segment; S415, respectively set the specified Δn of each task segment of gust overload and maneuvering overload y The exceedance numbers are summed to obtain the gust overload and its corresponding total exceedance number data pair. As well as the maneuvering overload and its corresponding total exceedance data and S416. For gust overload and maneuvering overload, sort the positive overloads and negative overloads at all levels by their absolute values ​​and match them to obtain the gust / maneuvering overload time series (n y,max , n y,min ) i .

7. The method for compiling a severe flight spectrum based on severe damage to mission profile according to claim 1, characterized in that: S42 includes: S421, according to the gust / maneuvering overload time series (n y,max , n y,min ) i The single cycle equivalent damage is calculated and accumulated to obtain the gust overload spectrum damage and maneuvering overload spectrum damage; the calculation formula is: Among them, Δn eq is the equivalent load cycle amplitude; Δn is the load cycle amplitude, and n is the load cycle peak value. y,max Subtract the load cycle valley value n y,min Then divided by 2 to get; m is the damage index; d eq is the single cycle equivalent damage, and D is the load spectrum damage; S422. Sum the gust overload spectrum damage and the maneuvering overload spectrum damage to obtain the total overload damage D eq .

8. The method for compiling a severe flight spectrum based on severe damage to a mission profile according to claim 1, characterized in that S5 include: S51. Calculate the coverage rate of each task segment at each level of overload as P L The transcendental number of is calculated as: Among them, μ i , σ i are respectively the i-th level overload n y,i The logarithmic median and standard deviation of the corresponding transcendental numbers, μ PL is the transcendence coverage ratio P L The corresponding quantile; S52, determine the data pairs (Δn y , ΔN PL ) i , and fitting is performed to obtain the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve corresponding to each mission segment, wherein the severe maneuvering overload cumulative exceedance curve includes: severe maneuvering positive overload cumulative exceedance curve and severe maneuvering negative overload cumulative exceedance curve; S53. Perform high-load interception and low-load elimination on the severe gust overload cumulative exceedance curve and severe maneuvering overload cumulative exceedance curve of each mission section.

9. The method for compiling a severe flight spectrum based on severe damage to a mission profile according to claim 1, characterized in that S6 include: S61, determining the load levels and corresponding cycle numbers of positive overload and negative overload of each task segment under each task profile, and the cycle numbers of positive overload and negative overload under each task segment are the same; S62. Taking the flight / glide flight spectrum with the highest overload as a reference, assuming that the highest overload in each flight / glide segment is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight; S63, linking various levels of overload and corresponding cycle numbers in a single takeoff and landing of the kth flight type of the i-th mission segment of the jth mission profile, randomly selecting peak values ​​and valley values ​​alternately for random pair arrangement, forming a severe overload spectrum of a specific flight type of the corresponding mission segment, and obtaining mission segment spectra of various flight types in each mission segment; S64, sorting the mission segment spectrum under each mission profile according to the mission segment sequence under the mission profile, to obtain a mission profile spectrum under a complete flight; S65. Sort all mission profile spectra of each complete flight to obtain a final flight overload spectrum.

10. The method for compiling a severe flight spectrum based on severe damage to mission profile according to claim 1, characterized in that: In S63, the load spectrum of each mission segment for a specific flight type is denoted as (Δn ydn ,-Δn ydm ) h , and the task segment spectrum is expressed as the following load pair sequence: f i,j,k =(Δn ydn ,-Δn ydm ) h In the formula, Δn ydn The nth (n=1, 2, 3, 4, 5) positive overload of the mission segment represents the peak overload; -Δn ydm It is the mth (m=1, 2, 3, 4, 5) level negative overload of this mission segment, representing the valley overload. For the level flight mission segment, the positive and negative overloads come from the gust / maneuvering overload. For the climb and descent mission segments, the positive and negative overloads come from the gust overload. For the departure and approach mission segments, the positive and negative overloads come from the maneuvering overload.

Images